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Yohannes
Yohannes

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Handwritten Digit Recognition System with Cloud and AI Enhancements

GitHub “Finish-Up-A-Thon” Challenge Submission

This is a submission for the GitHub Finish-Up-A-Thon Challenge

https://github.com/YohannesAH/Hand_Written_Recognize

What I Built

This is a handwritten digit recognition app where users draw Arabic numerals on an HTML canvas and click predict to identify them. The drawing is converted into a base64 image and sent via a POST request to a Flask backend. The backend preprocesses the image (grayscale conversion, thresholding, contour detection, noise filtering, dilation, and resizing) to match MNIST dataset format before passing it to a pre-trained ML model, which then predicts the digit(s).

Demo

https://github.com/YohannesAH/Hand_Written_Recognize/blob/main/docs/assets/demogif/sample.gif

The Comeback Story

My idea for the unfinished part of the project is to integrate Azure AI text-to-speech with multilingual support for the recognized digits. I also plan to improve scalability by using a serverless architecture and deploying the application on AWS using Kubernetes.

My Experience with GitHub Copilot

GitHub Copilot supported my process by helping me write and refine code faster. It suggested useful boilerplate, function structures, and helped debug small issues in both JavaScript and Python. It also helped me understand different implementation approaches, especially when building the Flask backend and preprocessing pipeline for the ML model. However, I reviewed and adjusted all suggestions to make sure they fit my project requirements and logic

Note:CNN(Convulational-Neural-Network-CNN-) https://github.com/vzhou842 was the sole contributor "https://github.com/vzhou842" which I cloned, modified, and retrained for this implementation.

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